3: Power and Presence

The arrival of intelligent machines is paced by the growth of
computing power. A
comparison between edge and motion detectors in the human retina
with similarly functioning computer vision programs suggests that the
retina does the job of 1,000 MIPS (million of instructions per second)
of computing. The whole brain is 100,000 times larger than the retina,
so is worth perhaps 100 million MIPS of efficient computation. The
following diagram rates other entities.

Power and Capacity

MIPS and Megabytes: Entities rated by the computational power
and memory of the smallest general-purpose computer needed to mimic
their behavior. Note the scale is logarithmic on both axes: each
vertical division represents a thousandfold increase in processing
power, and each horizontal division a thousandfold increase in memory
size. General-purpose computers (marked by an *) can imitate other entities at their location in
the diagram, but the more specialized thinkers cannot. A 100 million
MIPS computer may be programmed not only to think like a human, but
also to imitate any other similarly-sized computer. But humans cannot
imitate 100 million MIPS computers--our general-purpose calculation
ability is below a millionth of a MIPS. Beep Blue's special-purpose
chips calculate chess moves like a 3 million MIPS computer, but its
general-purpose component can do only a thousand MIPS. Most of the
non-computer entities in the diagram can't function in a
general-purpose way at all. Generality is an almost magical property,
but it has costs. A general-purpose machine may use ten times the
resources as one specialized for a task. But if the task should
change, as it usually does in research, the general machine can simply
be reprogrammed, while the specialized machine must be replaced.

Information handling capacity in computers has been growing about ten
million times faster than it did in nervous systems during our
evolution. The power doubled every two years in the 1950s, 1960s and
1970s, doubled every 18 months in the 1980s, and is now doubling each
year.

Computer power: 1900-1997

Faster than exponential:
In three decades the doubling time has fallen from two years to one year.

Alas, for several decades the computing power found in advanced Artificial
Intelligence and Robotics systems has been stuck at insect brainpower
of 1 MIPS. While computer power per dollar fell rapidly during this
period, the money available fell just as fast. The earliest days of
AI, in the mid 1960s, were fuelled by lavish post-Sputnik defense
funding, which gave access to $10,000,000 supercomputers of the time.
In the post Vietnam war days of the 1970s, funding declined and only
$1,000,000 machines were available. By the early 1980s, AI research
had to settle for $100,000 minicomputers. In the late 1980s, the
available machines were $10,000 workstations. By the 1990s, much work
was done on personal computers costing only a few thousand dollars.
Since then AI and robot brainpower has risen with improvements in
computer efficiency. By 1993 personal computers provided 10 MIPS, by
1995 it was 30 MIPS, and in 1997 it is over 100 MIPS. Suddenly
machines are reading text, recognizing speech, and robots are driving
themselves cross country.

AI Computer power: 1900-1997

The long stall:
From 1960 to 1990 the cost of computers used in AI research declined, as
their numbers increased greatly. The dilution absorbed computer
efficiency gains during this period, and the power available to
individual AI programs
remained almost unchanged at 1 MIPS, barely insect power. AI computer
cost bottomed in 1990, and since then power has risen instead, to
several hundred MIPS by 1997. The major visible exception is
computer chess, whose prestige has lured the resources of major
computer companies, as well as the talents of special machine designers.
Less visible exceptions probably exist in high value competitive
applications, like petroleum exploration and intelligence gathering.

Progress in Computer Chess, a thin slice of AI

Agony to ecstasy:
In forty years, computer chess progressed from the lowest depth to the
highest peak of human chess performance. It took a handful of good
ideas, culled by trial and error from a larger number of
possibilities, an accumulation of previously evaluated game openings
and endings, good adjustment of position scores, and especially a
ten-million-fold increase in the number of alternative move sequences
the machines can explore. Note that chess machines reached world
champion performance as their (specialized) processing power reached
about 1/30 human, by our brain to computer measure. Since it is
plausible that Garry Kasparov (but hardly anyone else) can apply his
brainpower to the problems of chess with an efficiency of 1/30, the
result supports that retina-based extrapolation. In coming decades,
s general-purpose computer power grows beyond Deep Blue's specialized
strength, machines will begin to match humans in more common skills.